# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import gc
import random
import tempfile
import unittest

import numpy as np
import paddle
from PIL import Image

from ppdiffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNet2DConditionModel,
)
from ppdiffusers.transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from ppdiffusers.utils import load_image, slow
from ppdiffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    require_paddle_gpu,
)

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin

enable_full_determinism()


class StableDiffusionDiffEditPipelineFastTests(PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase):
    pipeline_class = StableDiffusionDiffEditPipeline
    params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"height", "width", "image"} | {"image_latents"}
    batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"image"} | {"image_latents"}
    image_params = frozenset([])
    image_latents_params = frozenset([])

    def get_dummy_components(self):
        paddle.seed(seed=0)
        unet = UNet2DConditionModel(
            block_out_channels=(32, 64),
            layers_per_block=2,
            sample_size=32,
            in_channels=4,
            out_channels=4,
            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
            up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
            cross_attention_dim=32,
            attention_head_dim=(2, 4),
            use_linear_projection=True,
        )
        scheduler = DDIMScheduler(
            beta_start=0.00085,
            beta_end=0.012,
            beta_schedule="scaled_linear",
            clip_sample=False,
            set_alpha_to_one=False,
        )
        inverse_scheduler = DDIMInverseScheduler(
            beta_start=0.00085,
            beta_end=0.012,
            beta_schedule="scaled_linear",
            clip_sample=False,
            set_alpha_to_zero=False,
        )
        paddle.seed(seed=0)
        vae = AutoencoderKL(
            block_out_channels=[32, 64],
            in_channels=3,
            out_channels=3,
            down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
            up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
            latent_channels=4,
            sample_size=128,
        )
        paddle.seed(seed=0)
        text_encoder_config = CLIPTextConfig(
            bos_token_id=0,
            eos_token_id=2,
            hidden_size=32,
            intermediate_size=37,
            layer_norm_eps=1e-05,
            num_attention_heads=4,
            num_hidden_layers=5,
            pad_token_id=1,
            vocab_size=1000,
            hidden_act="gelu",
            projection_dim=512,
        )
        text_encoder = CLIPTextModel(text_encoder_config)
        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
        components = {
            "unet": unet,
            "scheduler": scheduler,
            "inverse_scheduler": inverse_scheduler,
            "vae": vae,
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "safety_checker": None,
            "feature_extractor": None,
        }
        return components

    def get_dummy_inputs(self, seed=0):
        mask = floats_tensor((1, 16, 16), rng=random.Random(seed))
        latents = floats_tensor((1, 2, 4, 16, 16), rng=random.Random(seed))

        generator = paddle.Generator().manual_seed(seed)

        inputs = {
            "prompt": "a dog and a newt",
            "mask_image": mask,
            "image_latents": latents,
            "generator": generator,
            "num_inference_steps": 2,
            "inpaint_strength": 1.0,
            "guidance_scale": 6.0,
            "output_type": "np",
        }
        return inputs

    def get_dummy_mask_inputs(self, seed=0):
        image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed))
        image = image.cpu().transpose(perm=[0, 2, 3, 1])[0]
        image = Image.fromarray(np.uint8(image)).convert("RGB")

        generator = paddle.Generator().manual_seed(seed)
        inputs = {
            "image": image,
            "source_prompt": "a cat and a frog",
            "target_prompt": "a dog and a newt",
            "generator": generator,
            "num_inference_steps": 2,
            "num_maps_per_mask": 2,
            "mask_encode_strength": 1.0,
            "guidance_scale": 6.0,
            "output_type": "np",
        }
        return inputs

    def get_dummy_inversion_inputs(self, seed=0):
        image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed))
        image = image.cpu().transpose(perm=[0, 2, 3, 1])[0]
        image = Image.fromarray(np.uint8(image)).convert("RGB")
        generator = paddle.Generator().manual_seed(seed)
        inputs = {
            "image": image,
            "prompt": "a cat and a frog",
            "generator": generator,
            "num_inference_steps": 2,
            "inpaint_strength": 1.0,
            "guidance_scale": 6.0,
            "decode_latents": True,
            "output_type": "np",
        }
        return inputs

    def test_save_load_optional_components(self):
        if not hasattr(self.pipeline_class, "_optional_components"):
            return
        components = self.get_dummy_components()
        pipe = self.pipeline_class(**components)
        pipe.set_progress_bar_config(disable=None)
        for optional_component in pipe._optional_components:
            setattr(pipe, optional_component, None)
        pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components})
        inputs = self.get_dummy_inputs()
        output = pipe(**inputs)[0]
        with tempfile.TemporaryDirectory() as tmpdir:
            pipe.save_pretrained(tmpdir, to_diffusers=False)
            pipe_loaded = self.pipeline_class.from_pretrained(tmpdir, from_diffusers=False)
            pipe_loaded.set_progress_bar_config(disable=None)
        for optional_component in pipe._optional_components:
            self.assertTrue(
                getattr(pipe_loaded, optional_component) is None,
                f"`{optional_component}` did not stay set to None after loading.",
            )
        inputs = self.get_dummy_inputs()
        output_loaded = pipe_loaded(**inputs)[0]
        max_diff = np.abs(output - output_loaded).max()
        self.assertLess(max_diff, 0.0001)

    def test_mask(self):
        components = self.get_dummy_components()
        pipe = self.pipeline_class(**components)
        pipe.set_progress_bar_config(disable=None)
        inputs = self.get_dummy_mask_inputs()
        mask = pipe.generate_mask(**inputs)
        mask_slice = mask[(0), -3:, -3:]
        self.assertEqual(mask.shape, (1, 16, 16))
        expected_slice = np.array([0, 0, 0, 1, 1, 0, 0, 0, 0])
        max_diff = np.abs(mask_slice.flatten() - expected_slice).max()
        self.assertLessEqual(max_diff, 0.001)
        self.assertEqual(mask[0, -3, -4], 0)

    def test_inversion(self):
        components = self.get_dummy_components()
        pipe = self.pipeline_class(**components)
        pipe.set_progress_bar_config(disable=None)
        inputs = self.get_dummy_inversion_inputs()
        image = pipe.invert(**inputs).images
        image_slice = image[(0), (-1), -3:, -3:]
        self.assertEqual(image.shape, (2, 32, 32, 3))
        expected_slice = np.array(
            [0.41519523, 0.4390324, 0.42185277, 0.5275973, 0.5615453, 0.51692694, 0.50686157, 0.50174856, 0.47317967]
        )
        max_diff = np.abs(image_slice.flatten() - expected_slice).max()
        self.assertLessEqual(max_diff, 0.001)

    def test_inference_batch_single_identical(self):
        super().test_inference_batch_single_identical(expected_max_diff=0.005)

    def test_inversion_dpm(self):
        components = self.get_dummy_components()
        scheduler_args = {"beta_start": 0.00085, "beta_end": 0.012, "beta_schedule": "scaled_linear"}
        components["scheduler"] = DPMSolverMultistepScheduler(**scheduler_args)
        components["inverse_scheduler"] = DPMSolverMultistepInverseScheduler(**scheduler_args)
        pipe = self.pipeline_class(**components)
        pipe.set_progress_bar_config(disable=None)
        inputs = self.get_dummy_inversion_inputs()
        image = pipe.invert(**inputs).images
        image_slice = image[(0), (-1), -3:, -3:]
        self.assertEqual(image.shape, (2, 32, 32, 3))
        expected_slice = np.array([0.7061, 0.9805, 0.6064, 0.7288, 0.8416, 0.6517, 0.5912, 0.5704, 0.5726])
        max_diff = np.abs(image_slice.flatten() - expected_slice).max()
        self.assertLessEqual(max_diff, 0.001)


@require_paddle_gpu
@slow
class StableDiffusionDiffEditPipelineIntegrationTests(unittest.TestCase):
    def tearDown(self):
        super().tearDown()
        gc.collect()
        paddle.device.cuda.empty_cache()

    @classmethod
    def setUpClass(cls):
        raw_image = load_image(
            "https://bj.bcebos.com/v1/paddlenlp/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png"
        )
        raw_image = raw_image.convert("RGB").resize((768, 768))
        cls.raw_image = raw_image

    def test_stable_diffusion_diffedit_full(self):
        generator = paddle.Generator().manual_seed(seed=0)
        pipe = StableDiffusionDiffEditPipeline.from_pretrained(
            "stabilityai/stable-diffusion-2", safety_checker=None, paddle_dtype=paddle.float16
        )
        pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
        pipe.inverse_scheduler = DDIMInverseScheduler.from_config(pipe.scheduler.config)
        pipe.set_progress_bar_config(disable=None)
        source_prompt = "a bowl of fruit"
        target_prompt = "a bowl of pears"
        mask_image = pipe.generate_mask(
            image=self.raw_image, source_prompt=source_prompt, target_prompt=target_prompt, generator=generator
        )
        inv_latents = pipe.invert(
            prompt=source_prompt, image=self.raw_image, inpaint_strength=0.7, generator=generator
        ).latents
        image = pipe(
            prompt=target_prompt,
            mask_image=mask_image,
            image_latents=inv_latents,
            generator=generator,
            negative_prompt=source_prompt,
            inpaint_strength=0.7,
            output_type="np",
        ).images[0]
        expected_image = (
            np.array(
                load_image(
                    "https://bj.bcebos.com/v1/paddlenlp/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/pears.png"
                ).resize((768, 768))
            )
            / 255
        )
        assert np.abs((expected_image - image).max()) < 0.75

    def test_stable_diffusion_diffedit_dpm(self):
        generator = paddle.Generator().manual_seed(seed=0)
        pipe = StableDiffusionDiffEditPipeline.from_pretrained(
            "stabilityai/stable-diffusion-2", safety_checker=None, paddle_dtype=paddle.float16
        )
        pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
        pipe.inverse_scheduler = DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config)
        pipe.set_progress_bar_config(disable=None)
        source_prompt = "a bowl of fruit"
        target_prompt = "a bowl of pears"
        mask_image = pipe.generate_mask(
            image=self.raw_image, source_prompt=source_prompt, target_prompt=target_prompt, generator=generator
        )
        inv_latents = pipe.invert(
            prompt=source_prompt,
            image=self.raw_image,
            inpaint_strength=0.7,
            generator=generator,
            num_inference_steps=25,
        ).latents
        image = pipe(
            prompt=target_prompt,
            mask_image=mask_image,
            image_latents=inv_latents,
            generator=generator,
            negative_prompt=source_prompt,
            inpaint_strength=0.7,
            num_inference_steps=25,
            output_type="np",
        ).images[0]
        expected_image = (
            np.array(
                load_image(
                    "https://bj.bcebos.com/v1/paddlenlp/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/pears.png"
                ).resize((768, 768))
            )
            / 255
        )
        assert np.abs((expected_image - image).mean()) < 0.5
